Principal Component Analysis (PCA)

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• AI Solver
• ...find outliers
• Clustering
• Anomaly Detection
• Dimensional Reduction Algorithms
• T-Distributed Stochastic Neighbor Embedding (t-SNE)
• Independent Component Analysis (ICA) | University of Helsinki
  • Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) | S. Shimizu, P. Hoyer, A. Hyvarinen, and A. Kerminen - University of Helsinki
  • Greedy DAG Search (GDS) | Alain Hauser and Peter Biihlmann

a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis.

1. Center (and standardize) data
2. First principal component axis
   1. Across centroid of data cloud
   2. Distance of each point to that line is minimized, so that it crosses the maximum variation of the data cloud
3. Second principal component axis
   1. Orthogonal to first principal component
   2. Along maximum variation in the data
4. First PCA axis becomes x-axis and second PCA axis y-axis
5. Continue process until the necessary number of principal components is obtained